Artificial Intelligence predicts the risk of recurrence of breast cancer, highlights ESMO study

Breast cancer is still a scary ailment in spite of all the advancements in technology

As per a report by World Health Organization, in 2020, there were 2.3 million women diagnosed with breast cancer and 685,000 deaths globally. At the end of 2020, there were 7.8 million women alive who were diagnosed with breast cancer in the past 5 years, making it the world’s most prevalent cancer. While detection at an early stage has been helpful in treatment, an overwhelmingly large number of breast cancer patients still live under the fear of recurrence. Breast cancer recurrence is a huge problem and extremely devastating for the patients. This is also the principal cause of deaths among breast cancer patients. Researchers over the years have tried to predict some sort of pattern for breast cancer recurrence but nothing concrete has been established till now.

However, as per a recent study by Gustave Roussy and the startup Owkin that was presented at ESMO 2021 (16th Sep – 21st Sep) (European Society of Medical Oncology) breast cancer recurrence can be predicted with the help of Artificial Intelligence.

This study called the RACE AI is part of the AI for Health Challenge organized by the Ile-de-France Region in 2019. It showed that with the help of deep learning analysis, artificial intelligence can classify patients with localized breast cancer between high risk and low risk of metastatic relapse in the next five years. Thus AI can become an aid to therapeutic decision making and avoid unnecessary chemotherapy and its impact on personal, professional and social lives for low risk women. This is one of the first proofs of concept illustrating the power of an AI model for identifying parameters associated with relapse that the human brain could not detect.

RACE AI is a retrospective study that was conducted on a cohort of 1400 patients managed at Gustave-Roussy between 2005 and 2013 for localized hormone-sensitive (HR+, HER2-) breast cancer. These women were treated with surgery, radiotherapy, hormone therapy, and sometimes chemotherapy to reduce the risk of distant relapse. 

Chemotherapy is not administered generally because not all women will benefit from it due to a naturally favorable prognosis. The practitioner’s choice is based on clinico-pathological criteria (age of the patient, size and aggressiveness of the tumor, lymph node invasion, etc.) and the decision to administer adjuvant chemotherapy or not varies between oncology centers. 

In the RACE AI study, Owkin’s Data Scientists, guided by Gustave Roussy’s research physicians, developed an AI model capable of reliably assessing the risk of relapse with an AUC of 81% to help the practitioner determine the benefit/risk balance of chemotherapy. This calculation is based on the patient’s clinical data combined with the analysis of stained and digitized histological slides of the tumor. These slides, used daily in pathology departments by anatomo-pathologists, contain very rich and decisive information for the management of cancer. It is not necessary to develop a new technique or to equip a specific technical platform. The only essential equipment is a slide scanner, which is a common piece of equipment in laboratories. Like an office scanner that digitizes text, this scanner digitizes the morphological information present on the slide.